code: def test_gradcheck(self, dimension, device, dtype):
      batch_size = 3
      num_points = 5
      points = torch.rand(
          (batch_size, num_points, dimension),
          device=device,
          dtype=dtype,
      )
      points = tensor_to_gradcheck_var(points)
  
      assert gradcheck(
          _mean_isotropic_scale_normalize,
          (points,),
          raise_exception=True,
          fast_mode=True,
      )
  
file_path: kornia/test/geometry/calibration/test_pnp.py
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  from kornia.geometry.calibration.pnp import _mean_isotropic_scale_normalize
  from kornia.testing import assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 2 for calling the TestNormalization obj's underlying member method code with example usage: obj.test_gradcheck(dimension, device, dtype) without return types